{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   User ID          400 non-null    int64 \n",
      " 1   Gender           400 non-null    object\n",
      " 2   Age              400 non-null    int64 \n",
      " 3   EstimatedSalary  400 non-null    int64 \n",
      " 4   Purchased        400 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User ID            0\n",
       "Gender             0\n",
       "Age                0\n",
       "EstimatedSalary    0\n",
       "Purchased          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15691539.76</td>\n",
       "      <td>37.66</td>\n",
       "      <td>69742.50</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>71658.32</td>\n",
       "      <td>10.48</td>\n",
       "      <td>34096.96</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15566689.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15626763.75</td>\n",
       "      <td>29.75</td>\n",
       "      <td>43000.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15694341.50</td>\n",
       "      <td>37.00</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15750363.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>88000.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15815236.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User ID     Age  EstimatedSalary  Purchased\n",
       "count       400.00  400.00           400.00     400.00\n",
       "mean   15691539.76   37.66         69742.50       0.36\n",
       "std       71658.32   10.48         34096.96       0.48\n",
       "min    15566689.00   18.00         15000.00       0.00\n",
       "25%    15626763.75   29.75         43000.00       0.00\n",
       "50%    15694341.50   37.00         70000.00       0.00\n",
       "75%    15750363.00   46.00         88000.00       1.00\n",
       "max    15815236.00   60.00        150000.00       1.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age vs EstimatedSalary')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.plot(df['Age'], df['EstimatedSalary'], 'o')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('EstimatedSalary')\n",
    "plt.title('Age vs EstimatedSalary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Purchased'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "df['Purchased'].value_counts().plot.bar(figsize=(6, 8), rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Purchased\n",
       "0    257\n",
       "1    143\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Purchased'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5800\\1232127755.py:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if Gender[i] is 'Male':\n"
     ]
    }
   ],
   "source": [
    "Gender = copy.deepcopy(df['Gender'])\n",
    "for i in range(len(Gender)):\n",
    "    if Gender[i] is 'Male':\n",
    "        Gender[i] = 1\n",
    "    else:\n",
    "        Gender[i] = 0\n",
    "df['Gender'] = copy.deepcopy(Gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
